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All Journal KOMUNIKE: Jurnal Komunikasi Penyiaran Islam KEUNIS Journal of Natural Science and Integration JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Ilmiah Dinamika Sosial Jurnal Ilmiah Manajemen dan Bisnis Jurnal Ilmiah Akuntansi dan Bisnis Open Access Indonesia Journal of Social Sciences Jurnal Analisis Hukum Journal of Education and Teaching Learning Open Access Indonesia Journal of Social Sciences Journal of Technology and Informatics (JoTI) TIERS Information Technology Journal Jurnal Ilmiah Telsinas Elektro, Sipil dan Teknik Informasi International Journal Software Engineering and Computer Science (IJSECS) Journal of Digital Learning and Distance Education International Journal on Advanced Technology, Engineering, and Information System (IJATEIS) Journal of Artificial Intelligence and Digital Business Room of Civil Society Development APLIKATIF: Journal of Research Trends in Social Sciences and Humanities Indonesian Journal of Multidisciplinary on Social and Technology International Journal of Multidisciplinary Approach Research and Science Journal of Education Method and Learning Strategy Educative: Jurnal Ilmiah Pendidikan Jurnal Ilmiah Multidisiplin Indonesia Jurnal Riset Multidisiplin dan Inovasi Teknologi Journal of Community Service and Society Empowerment AMPLITUDO: Journal of Science & Technology Innovation Journal of Social Science Utilizing Technology Journal of Language Education MANDALIKA: Journal of Social Sciences International Journal of Integrated Science and Technology Journal of Advanced Computer Knowledge and Algorithms Journal of Social Science Utilizing Technology EDUTREND: Journal of Emerging Issues and Trends in Education Room of Civil Social Development Digitus : Journal of Computer Science Applications Journal of Material Science and Radiation
Hakimi, Musawer
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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The Role of Artificial Intelligence in Achieving the UN Sustainable Development Goals (SDGs) in Low Income Nations Tarashtwal, Omid; Hakimi, Musawer; Naderi, Zuhoruddin
Jurnal Ilmiah Akuntansi & Bisnis Vol 10 No 2 (2025)
Publisher : Universitas Pendidikan Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38043/jiab.v10i2.7184

Abstract

Artificial Intelligence has been increasingly regarded as a transformative tool to pursue the United Nations' Sustainable Development Goals, especially in low income nations plagued by infrastructural, financial, and human resource constraints that hinder sustainable development. This paper analyzes the role of AI for economic development, social inclusion, environmental sustainability, and governance by highlighting pathways, synergies, and enabling technologies. We carried out a systematic literature review based on peer reviewed journal articles published between 2020 and 2025. We searched in IEEE Xplore, Emerald Insight, MDPI, ScienceDirect, and SpringerLink databases. In total, 30 articles that were relevant to the topic, were of sufficiently high methodological quality, and were applicable to this study were included in the review. Data were extracted on the use of AI, targeted SDGs, geographic location, and key findings. Bibliometric analyses and various approaches to thematic synthesis were used to better understand research trends, keyword cooccurrence, cross SDG synergies, and newly identified challenges. Results indicate that AI improves poverty reduction, financial inclusion, optimization of the workforce, and industrial innovation; improves education, gender equality, and social equity; climate monitoring, resource management, and urban sustainability; and governance and effective partnership with regards to transparency and informed decision making. Challenges pertain to infrastructure deficits, capacity gaps, and ethical considerations. Advice for policy development, capacity building, and responsible AI deployment underpin the need for context sensitive approaches. Artificial Intelligence arises as a key enabler of integrated, scalable, and sustainable development in low income countries.
Examining Cybersecurity Factors Affecting the Adoption and Institutionalization of Internet of Things Technologies in Developing Countries Hakimi, Musawer; Abdul Wajid Fazil; Zainullah Matin
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 1 (2026): Journal of Advanced Computer Knowledge and Algorithms - January 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i1.25505

Abstract

The Internet of Things promises transformative benefits for developing countries, ranging from fairly mundane efficiency improvements to markedly enhanced service delivery, yet actual adoption and long-term institutionalization remain slow and decidedly uneven, largely because of persistent security challenges. Privacy breaches, weak authentication, network vulnerabilities, and generally low levels of trust repeatedly emerge as decisive barriers, particularly in resource-constrained environments where even small failures can, in fact, undermine confidence quite severely. This study addresses the gap in synthesizing the security determinants that influence both the adoption and the deeper embedding of IoT technologies. A systematic literature review, guided by PRISMA, was conducted across IEEE Xplore, Scopus, Web of Science, SpringerLink, ACM Digital Library, and Taylor & Francis Online, identifying 25 peer-reviewed studies published between 2020 and 2025. Data extraction focused on security determinants, sectoral focus, regional distribution, and adoption patterns, so the analysis would retain a clear and coherent scope. Deductive coding covering privacy, authentication, and network security was combined with inductive themes related to trust and risk perception, and the findings were synthesized through frequency counts, thematic analysis, and cross-tabulation. Results highlight four dominant security clusters: privacy and confidentiality, trust and risk perception, authentication and access control, and network or infrastructure security. Privacy concerns were most frequently reported, followed quite closely by trust, authentication, and network vulnerabilities. Healthcare and education sectors appear most sensitive to privacy, while Asia dominates the evidence base, with Africa and Latin America still underrepresented. The study concludes that security concerns, while sometimes manageable in pilot phases, become critical barriers to scaling and institutionalization, so policymakers must priorities robust governance, trust-building, and capacity development to realize IoT’s potential in developing-country contexts.
Optimizing Traditional Games as Learning Media: A Literature Review on Cultural and Character Education Harpina, Deya; Hakimi, Musawer
MANDALIKA : Journal of Social Science Vol. 3 No. 1 (2025): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/mandalika.v3i1.180

Abstract

Using basic, environment-appropriate equipment and the results of cultural research that have been passed down from generation to generation in an area, traditional games may amuse kids. In this instance, integrating traditional games into education may be a unique set of values in the preservation, comprehension, and upkeep of cultural values while educating youngsters and having an effect on their characteristics. To successfully and meaningfully include conventional games into a child's educational environment. This entails modifying and improving conventional games so that kids may utilize them as useful teaching aids. Therefore, the goal of this essay is to further study the theory around the optimization of classic games as learning tools. This research's data was gathered by employing the literature study research method from a variety of literature sources that are pertinent to the subject or research issue under consideration. A thorough review and synthesis of pertinent studies has also been done in this study's literature study. The findings demonstrate that we may make learning interesting, participatory, and relevant for kids by utilizing the possibilities of traditional games. Challenges that could appear include a centralized curriculum, a modern viewpoint that disregards traditional games, logistical and facility issues, as well as community acceptance and involvement. These obstacles may be addressed, though, through understanding, and cooperation between parents, educators, and the community, as well as initiatives to imaginatively incorporate traditional games into children's learning.
Green Artificial intelligence Foundations, Applications, and Pathways to Sustainable Development Hakimi, Musawer; Tarashtwal, Omid; Ghafory, Hamayoon
AMPLITUDO : Journal of Science and Technology Innovation Vol. 5 No. 1 (2026): February
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/amplitudo.v5i1.524

Abstract

The fast evolution of artificial intelligence (AI) systems has worried people about their environmental impact thus prompting the rise of Green AI. In the present systematic review, we are going through the 32 articles published in peer-reviewed journals that were analyzed based on PRISMA standards regarding the conceptual bases, applications, and the future of Green AI. The review identified three paradigms: Green AI (computational efficiency), Sustainable AI (holistic socio-technical responsibility), and AI for Green (AI applied to sustainability challenges). A large part of the resources that would be used for the environments, monitoring, agriculture, and smart city applications can be saved by 15-30% through Green AI. The main difficulties are performance and efficiency balancing, limiting budget, and a research mentality that values precision more than sustainability. The research points out the dual function of AI in environmental matters as that of polluter and of a device for making the planet greener through humane practices and technologies. To sustainable AI, efficient algorithm design, regulatory support, the establishment of carbon-aware metrics, and collaboration among different disciplines to create the adoption of AI that is both economical and ethical are needed
EVALUATING USER EXPERIENCE OF THE UNDIKNAS MOBILE APPLICATION WITH USER EXPERIENCE QUESTIONNAIRE (UEQ) Agustini, Ni Wayan Eva; Sudestra, I Made Ardi; Gunawan, I Made Agus Oka; Indrawan, Gede; Dantes, Gede Rasben; Pidada, Ida Ayu Iswari; Hakimi, Musawer
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9350

Abstract

This research aims to evaluate the User Experience (UX) of the Undiknas Mobile application, developed to enhance the academic experience of students at Universitas Pendidikan Nasional (Undiknas). The primary focus of this study is to identify the factors that influence user satisfaction and dissatisfaction, as well as determine the areas that need improvement to enhance the application's UX quality. The research employs the User Experience Questionnaire (UEQ) as the primary tool to assess six key dimensions of user interaction: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. Data were collected from 309 respondents, consisting of students from the 2021 cohort of Universitas Pendidikan Nasional (Undiknas), specifically those in their final semester with more experience in using mobile applications. The respondents were selected to ensure that the data collected is valid and representative. The results show that the application achieved average scores of 1.20 for Attractiveness, 1.34 for Perspicuity, and 1.19 for Stimulation, all of which indicate above-average ratings. However, the Efficiency aspect received a lower score of 0.82, and Novelty scored only 0.73, suggesting that these areas need improvement, particularly in enhancing the app’s speed and adding innovative features. These findings indicate that improving the UX design, with a focus on boosting speed and introducing new features, could significantly enhance user satisfaction and engagement. This research provides valuable contributions to the development of digital academic services in Indonesian higher education, offering insights that can be applied to improve the mobile application's interface and functionality.
IMPROVING DIGITAL LEARNING: EVALUATING THE U LEARN LMS WITH THE SYSTEM USABILITY SCALE Sudestra, I Made Ardi; Agustini, Ni Wayan Eva; Gunawan, I Made Agus Oka; Indrawan, Gede; Hakimi, Musawer
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 4 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i4.6910

Abstract

The assessment of the usability of online learning systems is becoming more and more critical in order to ensure that users have the best possible experience. This study employed the System Usability Scale (SUS) to assess the usability of the learning management system at UNDIKNAS, using 68 students from various academic programs as participants. The employed methodology is administering a SUS questionnaire consisting of 10 items on a Likert scale from 1 to 5. The points are computed using the standard SUS methodology and multiplied by 2.5 to derive the final score. The analysis disclosed an average SUS score of 56.65, falling short of the academic usability benchmark 68. The score distribution visualization indicated that most respondents rated between 50 and 60, highlighting the need for system improvement. The SUS assessment of the learning management system at UNDIKNAS reveals that the system's usability necessitates improvement. Key recommendations include improving the user interface, optimizing navigation, and providing user guides to enhance the overall user experience
The Effectiveness of Assemblr Edu-Based Augmented Reality and Audio Media in Enhancing Science Concepts Understanding: A Quasi Experimental Design Safrizal, Safrizal; Arafah, Kavita; Selpia, Reva; Hakimi, Musawer
Journal of Natural Science and Integration Vol 9, No 1 (2026): Journal of Natural Science and Integration
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jnsi.v9i1.38994

Abstract

Conventional science instruction often relies on audio-based media, which often fails to help elementary students visualize complex, abstract scientific concepts, leading to suboptimal conceptual understanding. This study aimed to examine the effectiveness of Augmented Reality (AR) media in enhancing elementary students' conceptual understanding of science compared to audio-based media. A quasi-experimental design with a non-equivalent control group was employed, involving 50 fifth-grade students who were evenly assigned to experimental and control classes. Data were collected through pretests and posttests and analyzed using N-gain scores, effect size calculations, normality and homogeneity tests, and t-tests. The results showed that the experimental class achieved a mean pretest score of 41.6 and a posttest score of 67.2, while the control class achieved mean pretest and posttest scores of 42.6 and 49, respectively. The N-gain analysis showed greater improvement in the experimental class (0.44, moderate category) than in the control class. The effect size of 1.49 demonstrated a strong influence of AR-based media on students' learning outcomes. Furthermore, the t-test results confirmed significant differences both within groups (pretest–posttest) and between groups (posttest), emphasizing the superiority of AR media in science learning. It can therefore be concluded that Assemblr Edu-based AR media significantly improve elementary students' conceptual understanding of science. Keywords: augmented reality, students’ science concepts, science learning, audio media learning
Investigating the Integration of Big Data Technologies in Higher Education Settings Akrami, Khatera; Akrami, Mursal; Akrami, Fazila; Hakimi, Musawer
Indonesian Journal of Multidisciplinary on Social and Technology Vol. 2 No. 2 (2024): Maret - Juni
Publisher : PT Ilmu Data Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ijmst.v2i2.296

Abstract

The integration of big data technologies in higher education is a topic of growing interest due to its potential to revolutionize teaching, learning, and administrative processes. This study aims to explore the impact of big data technologies on educational practices and outcomes in higher education settings. Through a comprehensive investigation, including literature review, surveys, and statistical analysis, the study examines the utilization, effectiveness, and challenges associated with integrating big data technologies in educational settings. Key findings reveal a significant positive correlation between the utilization of big data technologies and the frequency of interaction among faculty, researchers, and practitioners. Additionally, faculty training is identified as a crucial factor influencing the successful integration of big data technologies in higher education. Institutional support emerges as a key facilitator in the effective implementation of big data technologies, while student readiness, including technological proficiency and willingness to engage, is found to positively correlate with integration efforts. The perceived effectiveness of big data technologies mediates the relationship between integration efforts and outcomes in higher education settings. Based on these findings, recommendations are provided to enhance the integration of big data technologies in higher education, including the need for continuous faculty training, institutional support, and student readiness initiatives. Overall, this study contributes to the ongoing discourse on leveraging data-driven approaches to enhance educational practices and outcomes in higher education.
Comparative Performance of Machine Learning Algorithms for Diabetes Prediction Sudestra, I Made Ardi; Gama, Adie Wahyudi Oktavia; Prathama, Gede Humaswara; Paramartha, I Gusti Ngurah Darma; Hakimi, Musawer
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1195

Abstract

Early detection of diabetes mellitus is crucial to prevent severe complications. This study evaluates three machine learning algorithms for diabetes prediction using a quantitative comparative experimental design. The algorithms are k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), and Random Forest. These methods were chosen to compare distinct learning paradigms. k-NN is distance-based, SVM is margin-based, and Random Forest is an ensemble method. The goal is to find the optimal model for clinical use. The Pima Indians Diabetes dataset was used. It includes 390 patients and 15 clinical features. Performance was measured by accuracy, precision, recall, and F1-score. Random Forest had the highest accuracy (89.7%) and F1-score, providing the most balanced classification. SVM followed with 84.6%, and k-NN achieved 76.9%. Although k-NN had the highest recall (0.750), its precision was low (0.375), showing a high false-positive rate. Feature importance analysis pointed to blood glucose levels as the most significant predictor, which matches clinical knowledge. In summary, ensemble techniques like Random Forest offer the most reliable results. This highlights the importance of selecting the right algorithm for early diabetes detection in clinical applications.
Predictive Modeling of Geohazards Using Artificial Intelligence: Earthquakes, Landslides, and Volcanic Risk Assessment Faqiri, Ahmad Fawad; Faqiri, Nasrin; Hakimi, Musawer
Journal of Material Science and Radiation Vol. 2 No. 1 (2026)
Publisher : Balai Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56566/jmsr.v2i1.706

Abstract

Geohazards such as earthquakes, landslides, and volcanic eruptions pose severe threats to human life and infrastructure, causing significant global losses every year. Existing hazard assessment methods are limited by single-hazard focus, high computational cost, sparse data integration, and poor real-time forecasting capabilities, which limit their operational use. This study aims to develop a unified artificial intelligence (AI) framework for multi-hazard forecasting by integrating convolutional neural networks (CNNs), long short-term memory (LSTM) models, random forest classifiers, and ensemble fusion techniques. A multi-source dataset consisting of seismic, geospatial, and geochemical data was processed using an 80/10/10 split train-validate-test, cross-validation, and spatial validation strategies. The results show strong performance, with earthquake classification AUC-ROC of 0.961, magnitude prediction RMSE of 0.23 Mw, landslide sensitivity AUC of 0.957, and volcanic classification accuracy of 91.2%, outperforming several state-of-the-art benchmarks. Ensemble fusion improved performance by 2.1–3.7% over individual models. The key contribution is a scalable ensemble-based AI framework that enables integrated multi-hazard forecasting on heterogeneous datasets. However, limitations include information heterogeneity and reduced cross-regional generalizability. The framework supports real-time early warning systems, disaster risk management, and land-use planning, especially in hazardous areas.
Co-Authors Abdul Wahid Samadzai Abdul Wajid Fazil Abidullah, Adel Adhi, Nurseto Adie Wahyudi Oktavia Gama Afifi, Sara Agustini, Ni Wayan Eva Ahmadi, Lima Ahmady, Ezatullah Akrami, Fazila Akrami, Khatera Akrami, Mursal Amir Kror Shahidzay Amiri, Frishta Amiri, Ghulam Ali Arafah, Kavita Aslamzai, Sebghatullah Azimy, Abdul Shakoor Barge Gul Khalili Behnaz Rahimi Danish, Jawad Daudzai, Mehriya Enayat, Wahidullah Ezam, Zakirullah Faqiri, Ahmad Fawad Faqiri, Nasrin Farid, Ahmad Salman Fazil, Abdul Wajid Fazila AKRAMI Frishta Amiri Frugh, Qurban Ali Gede Humaswara Prathama Gede Indrawan Gede Rasben Dantes Ghafory, Hamayoon Ghiasi, Zaynab Hakimi, Faiz Mohammad Hakimi, Nargis Hakimi, Samer Hakimi, Shohib Halimi, Yalda Hamidi, Shir Ahmad Harpina, Deya Hasas, Ansarullah Hejran, Muska Himmat, Bilal Hussaini, Mohammad Fawad I Gusti Ngurah Darma Paramartha I Made Agus Oka Gunawan I Wayan Aditya Suranata Ida Ayu Iswari Pidada Jawad Danish Katebzadah, Shairagha Khaliqyar, Khudai Qul Khani, Atefeh Mohammad Khatera AKRAMI Kohistani, Ahmad Jamy Maliha AHRARI Marpaung, Rosanna Mirwali Azizi Mohammad Aziz Rastagari Mohammad Mustafa Quchi Mohammadi, Farida Gul Mojadadi, Abdul Rahman Mursal AKRAMI Musaka Hejran Musawi, Sayed Zabihullah Naderi, Zuhoruddin Nadry, Zabihullah Najieb, Khairullah Naseri, Mohammad Fahim Nasrat, Abdulfatah Pangaribuan, Jontra Jusat Popal, Zekrullah Puspaningrum, Lintang Diah Qarizada, Abdulkhaliq Qasemi, Parisa Quchi, Mohammad Mustafa Quraishi, Tamana Quraishi, Tamann Quraishi, Tamanna Rahimi, Behnaz Rahimi, Nasrallah Rahmani, Khoshal Rahmani, Khoshal Rahman Rajaee, Sayed Mohammad Kazim Rastagari, Mohammad Aziz Rosyadi, Arfian Dwiki Safrizal Safrizal Sahnosh, Faqeed Ahmad Sajid, Saidamin Samadzai, Abdul Wahid Sediqi, Mateeullah Selpia, Reva Shahbazi, Hafizullah Shahidzay, Amir Kror Sirat, Abdul Wali Sudestra, I Made Ardi Sujacka Retno Tarashtwal, Omid Ulusi, Helena Wadeed, Wali Mohammad Zahid, Nabila Zainullah Matin Zarinkhail, Mohammad Shuaib Zarinkhail, Shuaib